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Clonorchiasis is an infectious disease caused by helminths of Clonorchis sinensis (C. sinensis). The adult parasite mainly inhabits the bile duct and gall bladder, and results in various complications to the hepatobiliary system. The amount of bile secreted into the intestine is reduced in cases of C. sinensis infection, which may alter the pH of the gut and decrease the amount of surfactant protein D released from the gallbladder. However, the impact of parasitic infection on the human gut microbiome remains unclear. To this end, we examined the gut microbiota composition in 47 modified Kato–Katz thick smear-positive (egg-positive) volunteers and 42 healthy controls from five rural communities. Subjects were grouped into four sub-populations based on age and infection status. High-throughput 16S rRNA gene sequencing revealed significant changes in alpha diversity between EP1 and EN1. The beta diversity showed alterations between C. sinensis-infected subjects and healthy controls. In C. sinensis infected patients, we found the significant reduction of certain taxa, such as Bacteroides and anti-inflammatory Bifidobacterium (P < 0.05). Bacteroides, a predominant gut bacteria in healthy populations, was negatively correlated with the number of C. sinensis eggs per gram (EPG, r = −0.37, P adjust < 0.01 in 20–60 years old group; r = −0.64, P adjust = 0.04 in the 60+ years old group). What’s more, the reduction in the abundance of Bifidobacterium, a common probiotic, was decreased particularly in the 60 + years old group (r = −0.50, P = 0.04). The abundance of Dorea, a potentially pro-inflammatory microbe, was higher in infected subjects than in healthy individuals (P < 0.05). Variovorax was a unique bacteria that was only detected in infected subjects. These results clearly demonstrate the significant influence of C. sinensis infection on the human gut microbiota and provided new insights into the control, prevention, diagnosis, and clinical study of clonorchiasis through the human gut microbiota.

The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives Affective Computing a large promotion. In this paper, we present our method of AU challenge in this Competition. We use improved IResnet100 as backbone. Then we train AU dataset in Aff-Wild2 on three pertained models pretrained by our private au and expression dataset, and Glint360K respectively. Finally, we ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU validation set.

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In this paper, we briefly introduce our submission to the Valence-Arousal Estimation Challenge of the 3rd Affective Behavior Analysis in-the-wild (ABAW) competition. Our method utilizes the multi-modal information, i.e., the visual and audio information, and employs a temporal encoder to model the temporal context in the videos. Besides, a smooth processor is applied to get more reasonable predictions, and a model ensemble strategy is used to improve the performance of our proposed method. The experiment results show that our method achieves 65.55% ccc for valence and 70.88% ccc for arousal on the validation set of the Aff-Wild2 dataset, which prove the effectiveness of our proposed method.